Clinical trials are a common mean in medical research to investigate new medications, medical devices and other medical products. Frequently, new studies are issued and the number of conducted clinical trials increases. The study register of the U.S. National Institutes of Health for example, which is one of the most frequently used public databases for clinical studies, contained approximately 23,500 clinical studies in 2006. In January 2008, the registered trials more than doubled to almost 50,000 studies from over 153 countries. Especially, the pharmaceutical industry and manufacturers of medical devices push the conduct of more and more studies. Therefore, the need for rapid and efficient but also accurate study planning, conducting and analysis of results is indispensable. Particularly the data collected during clinical trials is very valuable to the organizations controlling and executing the trials. Hence, the careful collection, handling and storage of the data while obeying national and international regulations are a major task in clinical study management.
Companies in the field of medical technology are concerned with the development of novel medical devices, which contribute to the development and improvement of diagnostics, therapy, prevention and monitoring of diverse diseases. For this purpose clinical trials and preliminary pilot trials are conducted. During the trials, data of interest is acquired in various formats by means of the developed devices, by questionnaires, case report forms and more. Afterwards the gathered data is processed and statistically analyzed to evaluate the quality and applicability of the novel devices. Furthermore, the coherence between acquired data and the disease progression as well as the health status of the patient is targeted. The results may be compared with data of devices, which are used in current medical practice or analyzed to gain new insights into certain disease progressions.
One way to manage clinical trials is to use ontologies. According to A Translation Approach to Portable Ontology Specification, Knowledge Acquisition 5:199-220, 1993, by Thomas Gruber, an ontology is a formal, explicit specification of a shared conceptualization. It describes a domain of interest in a machine readable and semantic way such that the concepts of the domain, relationships among them and constraints can be expressed such that a majority of a larger community agrees upon it.
Ontologies are used in the fields of artificial intelligence, knowledge engineering and the Semantic Web and are developed for variety of domains, including biomedicine or physics.
The modeling and use of ontologies in the field of database creation and data integration offers several benefits. Ontologies describe domains on a high abstraction level. The strengths of ontologies lie especially in the possibility to build a consistent and formal vocabulary, which cannot only be used for the definition of the structure and meaning of data stored in a database, but also be reused, to interoperate with and build applications based on this vocabulary.
Most of the existing systems are based on Entity-Attribute-Value (EAV) database design. This prohibits efficient database management through, e.g., indexing, partitioning, query optimization and thus hampers data analysis and ad-hoc querying.
Clinical trials are often conducted by multidisciplinary teams. An ontology based approach for creating trial databases provides a common basis on a high abstraction level, which allows users to design the databases conceptually independent of physical design issues such as indexes and keys.
Ontology-based system for clinical trial data management, IEEE Benelux EMBS Symposium, Dec. 6-7, 2007 of Geisler, S. et al describes an ontology based system for the design of clinical trial data management. A reference ontology serves as basis for the generation of clinical trial databases.
However, a drawback of the above mentioned system is that it cannot guarantee referential integrity, since some relational dependencies may be lost during the generation of the relational database. Furthermore, such a system is inefficient and lacks flexibility.
Hence, an improved method for creating a relational database schema from an ontology would be advantageous.